Method

Learning Distinguishable Representation for Pooled Grids in 3D Object Detection [RagNet3D]
[Anonymous Submission]

Submitted on 7 May. 2024 05:36 by
[Anonymous Submission]

Running time:0.05 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
In this work, we propose RoI-Aware Grids Pooling Networks for 3D
object detection, i.e., RagNet3D. Specifically, we devise RoI-
View Prediction Networks, simplified as RVPNet, to predict grid-
wise RoI-View residuals. Then, we also propose Couple-View
Fusion Networks, denoted as CoVFNet, to fuse the view-dependent
features and view-independent features, learned from predicted
RoI-View and 3D convolution networks respectively.
Parameters:
TBD
Latex Bibtex:
TBD

Detailed Results

Object detection and orientation estimation results. Results for object detection are given in terms of average precision (AP) and results for joint object detection and orientation estimation are provided in terms of average orientation similarity (AOS).


Benchmark Easy Moderate Hard
Car (Detection) 96.27 % 95.17 % 92.66 %
Car (Orientation) 96.26 % 95.04 % 92.48 %
Car (3D Detection) 88.74 % 81.91 % 77.45 %
Car (Bird's Eye View) 92.87 % 89.01 % 86.36 %
Cyclist (Detection) 90.12 % 81.20 % 74.57 %
Cyclist (Orientation) 89.25 % 80.23 % 73.69 %
Cyclist (3D Detection) 83.84 % 68.55 % 61.94 %
Cyclist (Bird's Eye View) 85.10 % 71.64 % 65.02 %
This table as LaTeX


2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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